Bibliographic record
Abstract
Subsequent to the discovery of insulin by Banting and Best in the Department of Physiology at the University of Toronto 100 years ago, the field of insulin signalling and action has grown at a remarkable pace. Yet, the recognition that insulin action in the brain is critical for whole body homeostasis has only recently been appreciated. The hypothalamus is a key region in the brain that responds to circulating insulin by engaging a complex signalling cascade resulting in the ultimate release of neuropeptides that control hunger and feeding. Disruption of this important feedback system can lead to a phenomenon called cellular insulin resistance, where the neurones cease to sense insulin. The factors contributing to insulin resistance, as well as the resulting detrimental effects, include the induction of neuroinflammation, endoplasmic reticulum stress and alterations in the architecture of the blood-brain barrier that allow transport of insulin into the brain. These manifestations usually change energy balance, causing weight gain, often resulting in obesity and its deadly comorbidities, including type 2 diabetes mellitus, cardiovascular disease and metabolic syndrome. Nonetheless, there is still hope because the signal transduction pathways can be targeted at a number of levels by neurone-specific therapeutics. With the advent of unique cell models for investigating the mechanisms involved in these processes, the discovery of novel targets is increasingly possible. Although we are still looking for a cure for diabetes, Banting and Best would be impressed at how far their discovery has advanced and the contemporary knowledge that has been accumulated based on insulin action.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".